A behavioral analysis tool that infers player motivations from in-game actions to drive personalized game design and feature prioritization.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install game-design-player-values-mapper
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install game-design-player-values-mapper using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Game Design Player Values Mapper?
The Game Design Player Values Mapper is a sophisticated framework designed for Openclaw Skills users to bridge the gap between telemetry and psychology. By treating player behavior as preference made visible, this skill helps developers move beyond surface-level demographics. It analyzes repetition, avoidance, and investment patterns to build a motivational structure that explains why players make specific choices.
This skill is essential for live-ops teams and game designers who need to understand the underlying value priorities of their audience. Whether you are designing re-engagement strategies or adjusting monetization surfaces, this tool provides a behavior-first profile that aligns game systems with actual player intent, ensuring that the design assumptions match the reality of the player experience.
Game Design Player Values Mapper Use Cases
- Designing personalized live-ops targeting and dynamic guidance systems.
- Segmenting players based on behavioral motivation rather than simple archetypes.
- Identifying why players are disengaging despite available rewards.
- Prioritizing feature development by understanding dominant value dimensions like Efficiency or Discovery.
- Refining mission framing and reward structures to resonate with specific player segments.
How Game Design Player Values Mapper Works
- Gather behavior signals: The agent lists concrete observations such as resource spending, session frequency, and ignored features.
- Map behaviors to values: Signals are translated into hypotheses across dimensions like Optimization, Aesthetics, or Social Status.
- Weight the value profile: A weighted matrix is created to assign levels (High/Medium/Low) and confidence scores to each inferred value.
- Detect tensions: The skill identifies contradictions where game design might be blocking a player's core values.
- Infer design alignment: The agent determines which content or incentives are likely to land well based on the profile.
- Form segment hypothesis: A practical, design-facing summary is generated (e.g., efficiency-first optimizer).
- Recommend actions: Actionable steps are provided to reframe, personalize, or adjust the game experience.
Game Design Player Values Mapper Setup
To integrate this capability into your workflow using Openclaw Skills, ensure your environment is configured to process behavioral markdown files.
# Example of initiating a player value analysis via CLI
openclaw run game-design-player-values-mapper --input player_telemetry.md
Game Design Player Values Mapper Data Schema & Taxonomy
The skill produces a structured report organized into the following metadata taxonomy:
| Section | Description |
|---|---|
| Observed Behavior Summary | Bulleted list of repeated and avoided actions. |
| Player Value Map | A table mapping values to Weight, Confidence, and Evidence. |
| Segment Hypothesis | A compact operational summary of the player pattern. |
| Design Implications | Analysis of system alignment and potential disengagement points. |
| Recommendations | Numbered list of strategic design changes. |
Game Design Player Values Mapper Advanced Features
- Multi-agent support for cross-referencing telemetry data with qualitative feedback.
- Conflict detection hooks to alert designers when progression mechanics punish a player's primary motivation.
- Adaptive mission framing logic for real-time personalization of narrative content.
- Fast mode execution for rapid prototyping during game jams or quick live-ops adjustments.
SKILL.md
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